The Most Popular Programming Languages and Their Applications

Data scientists are in high demand. The U.S. Bureau of Labor Statistics states that the overall job growth for computer and information research scientists is projected to be 19% from 2016 to 2026. Students pursuing careers in data science need to know how to use the most popular and appropriate programming language for each type of project.

To learn more, check out the infographic below created by Maryville University’s Bachelor’s in Data Science program.

How different programming languages impact data scientist roles.

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The Classics

A few programming languages have been around long enough to be widely considered the cornerstones of modern computer programming. These languages include Java, Python, C, SQL, and R.


Java’s popularity stems from its portability, user-friendliness, reusability, and the fact that its platform is independent at the source and binary levels. The language is a widely used choice to build Android apps, which are used for server-side applications for video games.


Python’s widely considered the easiest programming language to learn and it uses a simple syntax. This makes it a common language taught in school, not to mention the language used to build Pinterest and Instagram. Additionally, its Python Package Index has thousands of third-party modules for Python. That makes it useful for building web applications for data analysis and handy in software development used for building e-commerce systems.


Considered to be programming’s “common language,” C is popular because it can be used to create other programming languages such as Perl, Ruby, and R. It’s also used to speed up other languages, and is used by most operating systems like Android, iOS, Windows, Linux, and Mac. Because of this, it’s used for applications across every domain of programming.


SQL is popular because it forms the basis of many database management systems. Some of these systems include MySQL, Oracle, and PostgreSQL. It’s used for relational database management and data manipulation.


R is used as an open-source route to use the S language and includes a facility for effective data handling and storage. It also offers an integrated collection of intermediate tools for data analysis, and it features a fully planned and coherent system. It boasts a lot of uses, such as statistical computing, time-series analysis, graphics, and more.

The Newcomers

Developers of the newest programming languages have drawn from the strengths of established programming languages to overcome weaknesses and offer many useful benefits and features to the programming community.


Rust has gained attention because it performs better than PHP, Python, or JavaScript when writing low-level code. It also offers methods of programming that don’t cost substantial runtime overhead. Additionally, it supports modular programming and includes a package manager. Rust is typically used for web development.


Built for high performance, Julia offers great support for interactive use and is accessible to programmers of all backgrounds and experience levels. Finally, it’s an open source language and publicly viewable at GitHub. It’s used for deep learning, scientific computing, machine learning, data science, parallel and heterogeneous computing, and data visualization and plotting.


Elixir offers greater productivity than other non-java languages, and it’s used for a wide range of application development, such as the building of Android apps. It also offers many tools that automate tasks and increase efficiency.


Elm tends to be popular among those who prefer functional programming. It shows helpful error messages, which makes it easier to maintain than other languages. It also doesn’t have runtime exceptions, and it automatically enforces semantic versioning. It’s used for building user interfaces on the web.

Top Careers in Data Science

Students pursuing a career in data science can choose from numerous lucrative and rewarding occupations. These include: computer and information research scientists; computer systems analyst; operations research analyst; and database administrator.

Computer and Information Research Scientist

Those in computer and information research scientist roles are charged with inventing computing languages, methods, and tools, developing and advancing software systems, and assisting engineers and scientists in solving computing issues. The role’s projected job growth between 2016 and 2026 is 19%, and its 2018 median pay is $118,370.

Computer Systems Analyst

A computer systems analyst is tasked with conducting cost-benefit analyses of various IT systems, recommending ways to improve existing computer system functionality, and overseeing the installation and configuration of IT systems. The position’s projected 2016-2026 job growth is 9%, and its 2018 median pay is $88,470.

Operations Research Analyst

Individuals in operations research analyst positions collect and organize data from various sources, analyze info to identify problems and potential solutions, and use various methodologies to analyze data and develop solutions. The role’s projected job growth between 2016 and 2026 is 27%, and its 2018 median pay is $83,390.

Database Administrator

Database administrators oversee an organization’s data security, maintain database efficiency and proper functionality, and create and administer databases per user needs. The position’s projected 2016-2026 job growth is 11%, and its 2018 median pay is $90,070.


As worldwide dependence on technology grows, data science professionals will play a crucial role in building programs and applications to serve the needs of diverse individuals, communities, and organizations around the world. For this reason, data science will be the unifying language of the future.

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